Affective Computing

This chapter is dedicated to the study of the processes required to recognize, interpret, and process human affect. The most used affective models are studied, including a discussion on how computers can interpret them. The chapter covers the study of emotional theories (including evolutionary, physiological, neurological, and cognitive), universal emotions, valence, and arousal, learning-oriented emotions, as well as the basics of sentiment and affective states as a human motivator. On the field of personality, the chapter covers personality theories (Freud, trait, authoritarian), personality factors, and different scales that exist in psychology to categorize them. The different educational models related to learning and learning techniques are also presented. In addition, the process of cognition, the dynamics of the cognitive process, and the emotional motivators of such process at the same time will be discussed. We will also have a closing subchapter that discusses about emotions, sentiment, and personality analyzed through the machine as well as how can we use the emotions from a system application point of view and what a machine can learn from the emotional states. The aim of this chapter is to give background to the reader about personality, emotions, sentiment, and affect from a psychological point of view for its application in computation and how we can measure the different indicators of an existing condition in a user.